Data Literacy and the Colin Powell Rule

From frontline field support to back office operations

Colin Powell famously said that leaders should make critical decisions based on a defined zone of uncertainty. Acting with less than 40% of the data needed is reckless; waiting for more than 70% of the data maybe fatal. Balancing time and certainty is a challenge for all organizations.

Uncertainty is systemic in all business, and is not eliminated by policy, process or controls. The driver of uncertainty is unexpected change—in markets, competitors, society, technology, workforce, expectations, values—you name it. Businesses face change on a daily basis, and they must respond quickly.

Businesses cope with change, and the resulting uncertainty, through analytics. They study the situation, do root cause analysis, assess alternatives, and implement a response. The faster they can assemble enough data to do an analysis, the faster they can make a decision and respond. Per the Powell rule, they are always balancing speed and certainty. It’s not just the right decision, it’s the right decision in time.

The need to respond to change quickly is the driver for analytics agility. I’ve described in other columns ways to accelerate time-to-answer: with a data marketplace of self-serve data and user collaboration. But there’s another critical element to analytics agility: data literacy.

MIT defines data literacy as the ability to “read, work with, analyze and argue” with data. Any professional data scientist or systems analyst sees these skills as table stakes. But for real agility, organizations need data literacy across the workforce, from frontline field support to back office operations. The popular term is “democratization” of data and analytics, but few firms achieve it, and the impact is measurable.

Wharton and IHS Markit assessed the data literacy of over 600 companies on behalf of the Data Literacy Project, an independent consortium of industry organizations focused on the need for data literacy. The survey found that large enterprises with higher data literacy had enterprise values 3-5% higher than their peers. One contributor to this uplift is that data literate firms can more rapidly use analytics to respond to change.

For example, a few years ago a consumer products company instituted a voice-of-the customer program and created an analytics team to gather general customer feedback for product development. There was one week where complaints about one of their beverages in a region started to spike, and they used analytics to identify the source as a specific manufacturing plant. They called the plant manager and found it had switched suppliers of a key ingredient that month. After escalating the issue, the plant converted back to the original supplier and complaints dropped.

While this course-correction seems like something that should be an industry norm, it isn’t. Too often critical elements of the analytics supply chain are missing: a process to collect and synthesize real-world evidence, a data literate team able to spot an unexpected operational issue and identify its root cause, and that team being empowered to act directly based on their insights. Without these components, it is easy to imagine a regional dip in sales getting lost in aggregated sales reports, with management unable to recognize the problem and respond, resulting in many needlessly alienated customers. Multiply this across a large organization, and it is death by a thousand cuts.

Getting to a data literate workforce has three elements, all of which can be practically adopted:

  • Skill-building - There are online, free curricula to develop literacy at any level in the organization. While management can incent and recognize certifications, employees often embrace it as valuable professional development for the data economy.
  • Data assets - Data needs to be easy to find, understand and prepare for analytics. Today data marketplaces can be set up quickly and affordably on-premises or in the cloudto support analytics communities
  • Analytic tools - The other AI – augmented intelligence – uses analytics and machine learning to empower, not replace, human decision making. Modern tools are not just visual and interactive; the best use analytics to guide the analysis, interact with natural language instead of coding, and support collaboration among team members.

Data literacy not only makes the workforce more productive and motivated, it makes the organization more resilient. Responding to continuous change and its attendant uncertainty becomes business-as-usual, allowing the culture to pivot from being reactive to becoming confident innovators. Analytics agility drives business agility, allowing firms that achieve it to pull away from the pack.

@BarthPS discusses three methods to instill a data literate workforce.


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